# ggplotly(cases_1)
US %>% filter(Reported >= Sys.Date() - 60) %>% ggplot() + geom_col(aes(x=Reported,y=log(Deaths),fill=Deaths)) +
theme(axis.text.x = element_text(angle = 45)) +
labs(x="Date Reported",y="Log Deaths",title="COVID-19: Deaths by Date",
subtitle="(Logarithmic Scale)")
## Warning: Removed 10 rows containing missing values (geom_col).
JHU_US %>%
ggplot() + geom_col(aes(x=Date,y=na.omit(Recovered),fill=Recovered)) +
theme(axis.text.x = element_text(angle = 45)) +
labs(title="China COVID-19: Accumulated Recoveries by Date",x="Date Reported",y="Total Cases") + scale_y_continuous(labels = scales::comma) +
scale_fill_gradient(labels = scales::comma)
# WA <- JHU_US %>% group_by(Date) %>%
# summarise(Cases=sum(Cases), Deaths = sum(Deaths)) %>%
# mutate(daily_deaths = Deaths - lag(Deaths)) %>%
# mutate(daily_cases = Cases - lag(Cases)) %>%
# mutate(daily_recovered = Recovered - lag(Recovered)) %>%
# mutate(DeathRate = daily_deaths/daily_cases)
JHU_US %>% filter(Date >= Sys.Date() -60) %>%
ggplot() + geom_col(aes(x=Date,y=na.omit(Recovered),fill=Recovered)) +
theme(axis.text.x = element_text(angle = 45)) +
labs(title="USA COVID-19: Accumulated Recoveries by Date",x="Date Reported",y="Total Cases") + scale_y_continuous(labels = scales::comma) +
scale_fill_gradient(labels = scales::comma)